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Efrizoni, Lusiana
Program Studi Teknik Informatika, STMIK Amik Riau
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Public Health Social Sciences Other

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Optimization of Content Recommendation System Based on User Preferences Using Neural Collaborative Filtering Efrizoni, Lusiana; Junadhi, Junadhi; Agustin, Agustin
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i2.4775

Abstract

Recommender systems play a crucial role in enhancing user experience across various digital platforms by delivering relevant and personalized content. However, many recommender systems still face challenges in providing accurate recommendations, especially in cold-start situations and when user data is limited. This study aims to address these issues by optimizing content recommendation systems using Neural Collaborative Filtering (NCF), a deep learning-based approach capable of capturing non-linear relationships between users and items. We compare the performance of NCF with traditional methods such as Matrix Factorization (MF) and Content-Based Filtering (CBF) using the MovieLens-1M dataset. The research method employed is a quantitative approach that encompasses several stages, including preprocessing, model training, and evaluation using metrics such as Root Mean Squared Error (RMSE) and Precision@K. The results of this research are significant, demonstrating that NCF achieves the lowest RMSE of 0.870, outperforming MF with an RMSE of 0.950 and CBF with an RMSE of 1.020. Additionally, the Precision@K achieved by NCF is 0.73, indicating the model’s superior ability to provide more relevant recommendations compared to baseline methods. Hyperparameter tuning reveals that the optimal combination includes an embedding size of 16, three hidden layers, and a learning rate of 0.005. Despite its excellent performance, NCF still faces challenges in handling cold-start cases and requires significant computational resources. To address these challenges, integrating additional metadata and exploring regularization techniques such as dropout are recommended to enhance generalization. The implications of the findings from this study suggest that NCF can significantly improve prediction accuracy and recommendation relevance, thus having the potential for widespread application across various domains, such as e-commerce, streaming services, and education, to enhance user experience and the efficiency of recommendation systems. Further research is needed to explore innovative solutions to address cold-start challenges and reduce computational demands.
Co-Authors -, Dwi Haryono Afrinanda, Rizky Agung Marinda Agus Tri Nurhuda Agustin Agustin Agustin Agustin Agustin Agustin, Endy Wulan Ahmad - Fauzan Ahmad Fauzan Ahmad Rizali Anam, M Khairul Andhika, Imam Anthony Anggrawan Anugraha, Yoga Safitra Aprilia, Fanesa Arifin, Muhammad Amirul Armoogum , Sheeba Aulia, Rahma Azhari, Zahra Cikita, Putri Dadynata, Eric Dea Safitri Deni, Rahmad Devi Puspita Sari, Devi Puspita Dhini Septhya Djamalilleil, Said Azka Fauzan Edwar Ali Erlinda, Susi Ermy Pily, Annisa Khoirala ester nababan Fadly Fadly Farhan Pratama Farida Try Puspa Siregar Fatdha, Eiva Febrianda Putra Filza Izzati Finanta Okmayura Firdaus, Muhammad Bambang Firman, Muhammad Aditya Fransiskus Zoromi Fransiskus Zoromi, Fransiskus Habibie, Dedi Rahman Hadi Asnal, Hadi Hafsah Fulaila Tahiyat Handayani, Nadya Satya Haviluddin Haviluddin Helda Yenni, Helda Hidaya Spitri Iftar Ramadhan Ihsan, Raja Muhammad Ike Yunia Pasa Irwanda Syahputra Julianti, Nadea Junadhi Junadhi Junadhi Junadhi Junadhi, Junadhi Karpen Kartina Diah K. W. Kharisma Rahayu Koko Harianto Lathifah, Lathifah Lestari, Fika Ayu Lili Marlia M. Azzuhri Dinata M. Irpan Marhadi, Nanda Maulana, Fitra Melva Suryani Muhammad Bambang Firdaus Muhammad Oase Ansharullah Muhammad Syaifullah MUHAMMAD TAJUDDIN Munawir Munawir Muslim Muslim Nanda, Annisa Nasution , Zikri Hardyan Nurul fadillah, Nurul Oktavianda Oktavianda, Oktavianda Purnama, Muhammad Adji Putra, Febrianda Putri, Adinda Dwi Putri, Siti Faradila R. Guntur Surya Yuwana - Rahmaddeni Rahmaddeni Rahmaddeni, - Rahmiati Rahmiati Rais Amin Raja Muhammad Ihsan Ramadhani, Jilang Rati Rahmadani Ratna Andini Husen Revaldo, Bagus Tri Riadhil Jannah Rini Yanti, Rini Risky Harahap Risman Risman Rizki Astuti Rometdo Muzawi, Rometdo Sahelvi, Elza Salsabila Rabbani Sapina, Nur Sapitri, Riska Mela Sari, Atalya Kurnia Sarjon Defit Sarjon Defit Setiawan , Andri Shahreen Kasim, Shahreen Sholekhah, Fitriana Sularno Supian, Acuan Susandri, Susandri Susanti, Susanti Susi Erlinda Syahrul Imardi Syarifuddin Elmi Tahiyat, Hafsah Fulaila Tashid Tawa Bagus, Wahyu Torkis Nasution Tri Putri Lestari, Tri Putri Tri Revaldo, Bagus Triyani Arita Fitri Ulfa, Arvan Izzatul Unang Rio Uthami, Kurnia Vindi Fitria Wirta Agustin Wirta Wirta Yanti, Rini Yoyon Efendi Yulli Zulianda Zahra Azhari Zakaria , Mohd Zaki Zakaria, Mohd Zaki Zega, Wilman Zikri Hadryan nst Zulafwan Zuriatul Khairi Zuriatul Khairi